📁 Repo for python_splitter Python package. This package can split Images into Train, Test, Validation folders automatically by shuffling media/images for machine learning.

GraphPart, a data partitioning method for ML on biological sequences

Coursera Speccialization Courses

Neural Network based classifier that can determine whether a customer will leave the bank or not in the next 6 months

Machine Learning with Scikit Learn

Predicting The Energy Output Of Wind Turbine Based On Weather Condition DEMO LINK : https://youtu.be/ICfu49Ud2HU

To create a Decision Tree classifier and visualize it graphically, the purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.

Data preparation, statistical reasoning and machine learning are used to solve an unbalanced classification problem. Different techniques are employed to train and evaluate models with unbalanced classes.

different types of regression

This library allows reading and converting bounding box annotations in many popular formats

Model-Validation-Methods

classify the Size_Categorie using SVM

In this project, you work as a Data Scientist for a professional football club. The owner of the team is very interested in seeing how the use of data can help improve the team's performance, and perhaps win them a championship! The draft is coming up soon (that's when you get to pick new players for your team), and the owner wants you to create…

A time slicer for training and testing temporally correlated Machine Learning models.

This repository contains introductory notebook for logistic regression

To create a classification model to predict the gender (male or female) based on different parameters using given csv file.

The purpose of this project is to develop a machine learning model that predicts employee attrition (whether an employee will leave the company) and department assignment (which department an employee belongs to) based on various factors. These factors include age, travel frequency, education level, job satisfaction, marital status, and more.